My first instinct was just a hashmap for counts and then iterate everything on each query, which they let me code but then asked about the cost.
Start by clarifying requirements (e.g., scale, real-time vs batch, memory constraints) and then propose a data structure like a hash map to count logins and a min-heap or balanced BST to track one-time visitors by earliest login time. Discuss trade-offs between different approaches and how to extend to top X efficiently, considering both algorithmic complexity and system design aspects like distributed processing.
Pro tip: Demonstrate Amazon Leadership Principles by proactively discussing scalability, fault tolerance, and customer obsession—e.g., how the system would handle millions of users and ensure low-latency responses. Also, mention monitoring and metrics for operational excellence.
Ask questions to understand scale (number of users, login frequency), latency requirements, and whether the system needs to be real-time or can be batch. Clarify what 'earliest' means (timestamp granularity) and if ties need handling.
Propose using a hash map to count logins per user and a min-heap (priority queue) to track users with exactly one login, ordered by login time. Explain how to update these structures on each login event.
For top X, the min-heap can be maintained with size X, or use a balanced BST if X is large. Discuss how to efficiently retrieve the top X without scanning all users, and handle updates when a user logs in again (removing them from the one-time list).
Discuss distributed approaches (e.g., sharding by user ID, using a stream processing framework like Kafka and Flink) and trade-offs between memory usage, latency, and consistency. Mention alternative data structures like tries or order-statistic trees.
Recap the chosen approach, highlighting its efficiency (O(1) average update, O(log n) retrieval) and how it meets requirements. Mention potential optimizations and monitoring for production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.